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View all- Li XLiu YZhao H(2023)Saliency Detection Based on Low-Level and High-Level Features via Manifold-Space RankingElectronics10.3390/electronics1202044912:2(449)Online publication date: 15-Jan-2023
Image Saliency Detection via Multi-Feature and Manifold-Space Ranking
Authors: 
Xiaoli Li, Huaici Zhao, Yunpeng LiuAuthors Info & Claims
Xiaoli Li, Huaici Zhao, Yunpeng LiuAuthors Info & ClaimsPages 76 - 81
Abstract
In this paper, we propose an image saliency detection method by using multi-feature and manifold-space ranking. Basically, the proposed method extracts the color-histogram feature to obtain the fine information of the image, and the color-mean feature to obtain the coarse information respectively. To further improve the detection accuracy of the feature correlation between different image units, a manifold-space ranking method is used to calculate saliency values of image units to construct a saliency map on each feature-space. Specifically, we fuse the two saliency maps to obtain the final saliency map. Extensive experiments demonstrate that the proposed method not only outperforms the other methods, but also improves the accuracy and robustness of the saliency detection.
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- Image Saliency Detection via Multi-Feature and Manifold-Space Ranking
Index terms have been assigned to the content through auto-classification.
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January 2021
140 pages
ISBN:9781450388108
DOI:10.1145/3449365
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Published: 17 May 2021
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APIT 2021
APIT 2021: 2021 3rd Asia Pacific Information Technology Conference
January 15 - 17, 2021
Bangkok, Thailand
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View all- Li XLiu YZhao H(2023)Saliency Detection Based on Low-Level and High-Level Features via Manifold-Space RankingElectronics10.3390/electronics1202044912:2(449)Online publication date: 15-Jan-2023
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